Climate of the Past Discussions
www.clim-past-discuss.net
1814-9340
1814-9359
3
4
2007
10.5194/cpd-3-899-2007
http://www.clim-past-discuss.net/3/899/2007/
http://www.clim-past-discuss.net/3/899/2007/cpd-3-899-2007.html
http://www.clim-past-discuss.net/3/899/2007/cpd-3-899-2007.pdf
899
933
2007-07-06
Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe
M. Vrac
mathieu.vrac@lsce.ipsl.fr
D. Paillard
P. Naveau
Laboratoire des Sciences du Climat et de l'Environnement, LSCE, CEA/CNRS/UVSQ, Institut Pierre Simon Laplace (IPSL), Centre d'étude de Saclay, Orme des Merisiers, 91191 Gif-Sur-Yvette, France
The needs of small-scale climate information have become prevalent to study
the impacts of future climate change as well as for paleoclimate researches
where the reconstructions from proxies are obviously local. In this study we
develop a non-linear statistical downscaling method to generate local
temperatures and precipitation values from large-scale variables (e.g. Global Circulation Model – GCM – outputs), through Generalized Additive
Models (GAMs) calibrated on the present Western Europe climate. First,
various monthly GAMs (i.e. one model for each month) are tested for
preliminary analysis. Then, annual GAMs (i.e. one model for the 12 months
altogether) are developed and tailored for two sets of predictors
(geographical and physical) to downscale local temperatures and
precipitation.
<br><br>
As an evaluation of our approach under large-scale conditions different from
present Western Europe, projections are realized (1) for present North
America and Northern Europe and compared to local observations (spatial
test); and (2) for the Last Glacial Maximum (LGM) period, and compared to
local reconstructions and GCMs outputs (temporal test).
<br><br>
In general, both spatial and temporal evaluations indicate that the GAMs are
flexible and efficient tools to capture and downscale non-linearities
between large- and local-scale variables. More precisely, the results
emphasize that, while physical predictors alone are not capable of
downscaling realistic values when applied to climate strongly different from
the one used for calibration, the inclusion of geographical-type variables
– such as altitude, advective continentality and W-slope – into GAM
predictors brings robustness and improvement to the method and its local
projections.
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